Executive
Summary
Data quality is an elusive goal for most companies because it is
treated as a one-time event. No matter how well they do, the data
begins to decay immediately. Quality data is important to getting
value from enterprise applications, since up to half of the trouble
tickets logged by help desks are traced to master data errors. A strong data quality program
is also critical to success for strategic sourcing, customer management, and global
data synchronization (GDS).
As part of our ongoing research on master data management (MDM), AMR Research
has interviewed dozens of global companies on how they cleanse, enrich, and measure
the quality of data. Here’s what we found:
- Global ERP rollouts and emerging implementations of service-oriented architectures (SOAs) are forcing consolidation of data quality initiatives.
- Measuring data quality requires business rules.
- There are six major architectural approaches to using data quality software and services.
- Data quality and enrichment often require a combination of vendors.
The right data quality solution for your company is highly dependent not only on your
needs, but on the existing data architecture it is meant to serve. Approach data quality as
a part of your overall MDM strategy, starting with documenting your data architecture.